Quality control for point-of-care diagnostic systems

ABSTRACT

The present disclosure relates to quality control for point-of-care medical diagnostic systems. In various embodiments, the system includes an on-board storage containing a synthetic quality control material, a plurality of sub-systems having a plurality of operating parameters and including a material analyzer, a database storing quality control results that include results of the material analyzer analyzing the synthetic quality control material over time, one or more processors, and at least one memory storing instructions which, when executed by the one or more processors, cause the system to, automatically without user intervention: generate a control chart based on the quality control results, determine that a parameter of the plurality of operating parameters is out-of-tolerance based on the control chart, and adjust at least one of the plurality of sub-systems without user intervention to bring the out-of-tolerance parameter to within tolerance.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 16/368,929, filed on Mar. 29, 2019, which claims the benefit ofand priority to U.S. Provisional Patent Application No. 62/650,609,filed on Mar. 30, 2018. The entire contents of each of the foregoingapplications are hereby incorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates to medical diagnostics, and moreparticularly, to quality control for point-of-care medical diagnosticsystems.

BACKGROUND

Medical guidance for many medical diagnostic systems, such as hematologyanalyzers, recommends analyzing a sample as soon as possible afterdrawing the sample. This recommendation can be difficult if the sampleis obtained at the point of care but the test is to be performed at anexternal laboratory. Therefore, many doctors and veterinarians prefer tohave point-of-care (POC) systems to analyze fresh samples. On the otherhand, medical diagnostic systems rely on quality control procedures toconfirm system functionality and assure result accuracy. However,quality control procedures may not be familiar to POC offices, and thislack of familiarity can be a significant reason for doctors andveterinarians to send samples to external laboratories.

Hematology diagnostic systems have some of the most difficultrequirements for quality control and performance. Quality control (QC)for hematology systems can be especially difficult because there is ageneral belief in the medical and veterinary fields that hematology QCmust use fixed cells in order to accurately gauge system performance.Fixed cell quality control generally involves cells that have beenstabilized and mixed in predetermined concentrations. The cells can behuman or veterinary cells, which are commonly used to representdifferent cell types in whole blood.

The primary approach for hematology QC using fixed-cells generallyrequires refrigerated storage, with the fixed cells having a shelf-lifeof about eight-weeks. Additionally, fixed-cells have limited stabilityat room temperature, and thus, the operator must warm the sample priorto use and then return them to cold storage as soon as possiblethereafter. Also, after opening, fixed cells generally remain stable forabout two-weeks or less. The short shelf life and strict thermalrequirements of fixed cells often create doubt about the material when aQC test fails, requiring reruns with a separate lot of control materialto confirm the result. Another disadvantage of fixed cells is thathematology systems are designed to interact with cells in a particularchemical manner, and such interactions can be inhibited by techniquesused to stabilize cells for fixed-cell controls.

For veterinary diagnostic systems, fixed-cell quality control approachesoften have deficiencies when several veterinary species are supported.For veterinary diagnostics, there can be significant differences betweencells of different species, and therefore, each species will generallyhave its own cell recognition algorithm in the diagnostic system. Insuch systems, fixed cell quality control materials may not be able toconfirm system performance for all supported species. For example,canine sample analysis could satisfy quality control parameters, whilefeline sample analysis may not. In particular, fixed-cell qualitycontrol approaches may not be able to confirm the performance of certainsystem components, such as species-specific reagent reactions andspecies-specific fluidic and detection system problems.

Accordingly, there is continuing interest in improving medicaldiagnostic systems.

SUMMARY

The present disclosure relates to quality control for point-of-carediagnostic systems. In accordance with aspects of the presentdisclosure, an integration of on-board automated bead analysis,automated blank runs, and/or trended patient samples (by species),provides a new approach to determine not only that the diagnostic systemis in control, but also which component is failing if it is not incontrol.

In accordance with aspects of the present disclosure, a system forpoint-of-care medical diagnostics includes an on-board storagecontaining a synthetic quality control material, a plurality ofsub-systems having a plurality of operating parameters where thesub-systems include a material analyzer configured to analyze patientsamples and to analyze the synthetic quality control material, adatabase storing quality control results over time where the qualitycontrol results include results of the material analyzer analyzing thesynthetic quality control material over time, one or more processors,and at least one memory storing instructions which, when executed by theone or more processors, cause the system to, automatically without userintervention: generate a control chart based on the quality controlresults, determine that a parameter of the plurality of operatingparameters is out-of-tolerance based on the control chart, and adjust atleast one of the plurality of sub-systems without user intervention tobring the out-of-tolerance parameter to within tolerance. In variousembodiments, the instructions, when executed by the one or moreprocessors, cause the system to provide a visual indication to anoperator regarding the automatic adjustment.

In various embodiments, the database stores previous patient testresults that include results of the material analyzer analyzing samplesobtained from a plurality of patients over time. The instructions, whenexecuted by the one or more processors, cause the system to,automatically without user intervention: generate another control chartbased on the previous patient test results, determine that anotherparameter of the plurality of operating parameters is out-of-tolerancebased on the another control chart, and adjust at least one sub-systemof the plurality of sub-systems without user intervention to bring theanother out-of-tolerance parameter to within tolerance.

In various embodiments, the instructions, when executed by the one ormore processors, cause the system to, automatically without userintervention: determine that another parameter of the plurality ofoperating parameters is out-of-tolerance, determine that the anotherout-of-tolerance parameter requires user intervention to bring theanother out-of-tolerance parameter to within tolerance, and provide avisual indication informing an operator that the another parameter isout-of-tolerance.

In various embodiments, the instructions, when executed by the one ormore processors, cause the system to, automatically without userintervention: analyze a blank sample using the material analyzer wherethe material analyzer operates on the blank sample in a same manner thatthe material analyzer operates on a patient sample, determine that thematerial analyzer should be cleaned based on the analysis of the blanksample, and provide a visual indication informing an operator that thematerial analyzer should be cleaned.

In various embodiments, the instructions, when executed by the one ormore processors, cause the system to, automatically without userintervention: access the synthetic quality control material from theon-board storage, analyze the synthetic quality control material usingthe material analyzer to provide additional quality control results, andstore the additional quality control results in the database.

In various embodiments, the material analyzer is a hematology analyzer.In various embodiments, the material analyzer is at least one of: achemistry analyzer, a coagulation analyzer, or a urine analyzer.

In various embodiments, the material analyzer includes a flow cytometer.In various embodiments, the plurality of sub-systems includes a fluidicssub-system, an optics sub-system, and an electronics sub-system. Invarious embodiments, the plurality of operating parameters includeoptical density, flow rate, extinction channel (EXT), low angle forwardlight scatter channel (FSL), right angle scatter channel (RAS), highangle forward light scatter channel (FSH), and time-of-flight channel(TOF).

In accordance with aspects of the present disclosure, a system forpoint-of-care medical diagnostics includes an on-board storagecontaining a synthetic quality control material, a plurality ofsub-systems having a plurality of operating parameters and including amaterial analyzer configured to analyze patient samples and to analyzethe synthetic quality control material, a database, one or moreprocessors, and at least one memory. The database stores data includingquality control results over time that include results of the materialanalyzer analyzing the synthetic quality control material over time,previous patient test results that include results of the materialanalyzer analyzing samples obtained from a plurality of patients overtime, and blank sample results over time that include results of thematerial analyzer analyzing blank samples over time. The at least onememory stores instructions which, when executed by the one or moreprocessors, cause the system to, automatically without userintervention: generate at least one control chart based on the qualitycontrol results, the previous patient test results, and the blank sampleresults, determine that a parameter of the plurality of operatingparameters is out-of-tolerance based on the at least one control chart,and adjust at least one sub-system of the plurality of sub-systemswithout user intervention to bring the out-of-tolerance parameter towithin tolerance.

Further details and aspects of exemplary embodiments of the presentdisclosure are described in more detail below with reference to theappended figures.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an embodiment of quality controloperations, in accordance with aspects of the present disclosure;

FIG. 2 is a diagram of an exemplary control chart used for qualitycontrol, in accordance with aspects of the present disclosure;

FIG. 3 is a diagram of exemplary components of a flow cytometryanalyzer, in accordance with aspects of the present disclosure; and

FIG. 4 is a diagram of an exemplary plot of optical characteristics forvarious particles, in accordance with aspects of the present disclosure.

DETAILED DESCRIPTION

The present disclosure relates to quality control for point-of-caremedical diagnostic systems. As used herein, point-of-care refers to alocation where care is provided to human or animal patients, and amedical diagnostic system refers to a system that can analyze a sampleobtained from a patient to diagnose a medical condition of the patient.Accordingly, a medical diagnostic system includes a patient sampleanalyzer, such as, but not limited to, a flow cytometer.

Quality control in general involves having a diagnostic systemdemonstrate its performance on quality control (QC) materials, such thatappropriate performance on the QC materials correlates to appropriateperformance on patient samples. As will be described in detail herein,the proposed systems and methods relate to quality control operationsusing synthetic QC materials, patient-based quality control, and/orblank runs. Portions of the present disclosure will focus on veterinaryhematology analyzers, but the description herein applies to other typesof medical diagnostic systems as well, including, but not limited to,chemistry analyzers, coagulation analyzers, and urine analyzers.

Referring to FIG. 1 , there is shown a block diagram of an embodiment ofexemplary quality control procedures 100 for a medical diagnosticsystem. The quality control procedures 100 include a QC operation 102, ablank run operation 104, and/or a patient-data operation 106. Each ofthese operations 102-106 will be described in more detail later herein.The results of these operations 102-106 are stored in a database 108,and the information in the database 108 is then used to generate controlcharts 110. Control charts will be described in more detail later inconnection with FIG. 2 . Based on the control charts 110, the qualitycontrol procedures can determine whether various components of themedical diagnostic system are operating within intended parameters 112.If the system is operating within intended parameters 114, noadjustments are needed, and the system can run the quality controloperations 102-106 again when scheduled or requested to do so. If thesystem is not operating within intended parameters 116, the system canautomatically make adjustments where possible 118, and/or can alert anoperator to manually make adjustments when automatic adjustments are notpossible 120.

The following describes the quality control run 102 of FIG. 1 . Theblank run 104 and then patient run 106 will be described later herein.

A quality control run 102 involves the use of quality control (QC)materials. In accordance with aspects of the present disclosure, a QCmaterial is provided that is a synthetic non-biological material, butstill provides sensor responses that mimic or that are similar to sensorresponses for biological materials. In various embodiments, because theQC materials are synthetic, they can have longer shelf life than fixedcells. In various embodiments, the QC material is stable at roomtemperature and can be stored on-board the diagnostic system at roomtemperature. In various embodiments, the diagnostic system can store theon-board QC material at specified environmental conditions (such asrefrigeration or otherwise), and then handle them appropriately (e.g.,warm the material) when an automated run is desired. In variousembodiments, no action from the operator is needed to perform thequality control operations other than replenishing the on-board controlmaterial as needed.

In various embodiments, the QC material can be polymer beads forstandardization and calibration of a hematology flow cytometer. Anexample of polymer beads is disclosed in U.S. Pat. No. 6,074,879, whichis hereby incorporated by reference herein in its entirety, and whichpersons skilled in the art will understand. In various embodiments, thepolymer beads can include latex, polystyrene, polycarbonate, and/ormethacrylate polymers.

In a fixed-cell quality control material, even though the cells aresurrogates for natural patient cells, they have different chemicalbehavior compared to actual cells in natural samples. Accordingly, inthe medical diagnostic system, the classification of fixed-cells isperformed differently than the classification of patient samples, toaccount for these differences. In contrast, in accordance with aspectsof the present disclosure, the QC material can mimic or substantiallyresemble the cellular or chemical features that the medical diagnosticsystem is intended to count, measure, or analyze, such that the sameclassification methodology can be used for natural samples as well asfor the QC materials of the present disclosure.

In accordance with aspects of the present disclosure, the diagnosticsystem can automatically run the quality control operations 102-106. Forexample, the diagnostic system can include a feedback sub-system 100that works with the QC materials housed within the diagnostic system.Based on the QC materials and the feedback sub-system, the diagnosticsystem can determine whether its components are functioning withinintended parameters or whether adjustments are required. In variousembodiments, some adjustments can be performed automatically 118 by thediagnostic system. Such automatic adjustments can beneficially maintaindiagnostic accuracy and preempt significant diagnostic errors. Otheradjustments may require user interaction, and the diagnostic system canprovide an indication to the user regarding any such actions 120. Thus,the user receives the benefits of automated alerts with actionableguidance to maintain the diagnostic system. In various embodiments, thediagnostic system can provide an indication to the user regarding thediagnostic system's performance based on the quality control operations102-106.

In various embodiments, for adjustments that cannot be automaticallyperformed, the diagnostic system can communicate an electronic messageor report to the manufacturer or servicer for the diagnostic system. Themanufacturer or servicer can use such electronic messages/reports invarious ways. In various embodiments, the electronic messages can beused to schedule service for the diagnostic system. In variousembodiments, the electronic messages can be aggregated for multiplediagnostic systems and can be analyzed to determine performance trendsof various components of the diagnostic systems. Such information can beuseful for identifying areas that may benefit from design modificationsor improvements.

In various embodiments, quality control procedures 100 can beautomatically performed each day to keep the diagnostic systemwell-maintained. For example, the quality control procedures 100 canautomatically be performed at 2:00 AM each day, or at another time.Automated hematology analyzers in human medicine can perform qualitycontrol procedures 100 at least once per 8-hour shift, which is thefrequency generally required by governing agency regulation. Veterinaryhematology analyzers do not have regulatory requirements for qualitycontrol. Accordingly, veterinary offices may perform quality controlprocedures 100 less often. In various embodiments, the frequency ofrunning the quality control procedures 100 may depend on how often orhow seldom patient samples are analyzed. For example, veterinary officesmay run very few patient samples in a day, or as few as one sample perday. In such offices, running quality control procedures 100 once perday would double the cost of reagents used by the office. Accordingly,for such offices, the frequency of running the quality controlprocedures 100 may be less frequent. In various embodiments, veterinaryhematology analyzers may perform quality control analyses asinfrequently as once per month.

In accordance with aspects of the present disclosure, informationrelating to the quality control tests 102-106 can be stored in thedatabase 108. The database can be any type of database, such as a SQLdatabase, a NoSQL database, or another type of database.

In various embodiments, QC results can be plotted in control charts 110,such as a Levey-Jennings chart as shown in FIG. 2 , and can be comparedwith target values or ranges. In various embodiments, control chartrules can determine whether the system is in control or not 112. Invarious embodiments, a control chart 110 can be generated for multipleparameters to determine which parameter or parameters may be out ofcontrol and require corrective action 116, and which parameters are incontrol and require no changes 114.

In various embodiments, the quality control materials can be provided inpredetermined concentrations that enable three levels of control,including normal, high, and low levels. Having three levels allows theuser to confirm whether the diagnostic system is functioning properly todetect the normal range and the abnormal ranges. In various embodiments,each level can be shown in the control chart. The control charts candemonstrate the historical performance of the analyzer, as shown in FIG.2 , and can provide a way to detect when changes are needed, includingrelatively small changes. In various embodiments, the operator can havethe ability to access and view the control charts. In variousembodiments, a control chart need not be in the form of a chart as shownin FIG. 2 , and can be implemented in different ways. For example, invarious embodiments, a control chart can be implemented as anorganization of stored data values that are correlated with time orcorrelated with data sample number.

In various embodiments, calibration needs can be determined from thecontrol chart data. A technician can determine if an out-of-controlparameter requires a diagnostic system component to be re-calibrated, orwhether other actions should be taken instead, such as cleaning thecomponent. Generally, calibration changes are performed last, after allother functionality is confirmed.

Accordingly, described above herein are various aspects of qualitycontrol for medical diagnostic systems in general. The following willdescribe aspects of flow-cytometry-based diagnostic systems inparticular, and quality control for such systems. An example of aflow-cytometry-based analyzer is shown and described in U.S. Pat. No.7,324,194, which is hereby incorporated by reference herein in itsentirety, and which persons skilled in the art will understand.

Flow cytometry systems include sub-systems such as fluidics, optics, andelectronics sub-systems. Referring to FIG. 3 , a fluidics sub-systemarranges a sample into a stream of particles, such as a stream of cells.The optics sub-system examines each cell by directed a laser beam toeach cell and detecting scattered light using photo-detectors. Light isscattered according to size, complexity, granularity, and diameter ofthe cells, which form a “fingerprint” of each cell type. An example isshown in FIG. 4 . The electronics sub-system can process thefingerprints to classify, count, and/or otherwise analyze thecells/particles in the sample stream.

Flow cytometry systems have a series of settings and parameters thattune the fluidics, optics, and electronics sub-systems so that specificscatter patterns and positions will be produced from input samples. Whenthese sub-systems all function properly, the system is able to correlatethe scatter outputs with particular cells using recognition algorithms.However, if these parameters shift, the recognition algorithms can fail.Another level of tuning is part of the calibration process, wherevarious calibration parameters are used to tune output results to matchreference values for a given set of samples. As the diagnostic systemdrifts or shifts, the calibration parameters may need to be adjusted toensure that output results continue to match reference values.

In accordance with aspects of the present disclosure, a flow cytometerfor hematology can utilize quality control procedures (FIG. 1, 100 ) toensure that the major functions of the diagnostic system are operatingin a controlled manner, including yielding accurate and precise results.The following aspects and parameters of a hematology system can betested and trended by the quality control procedures of FIG. 1 .

-   -   Preanalytic: is the sample appropriately mixed. In various        embodiments, mixing of a quality control material can be        performed by an internal vortex mixer in the medical diagnostic        system. The vortex mixer can mix the quality control material        from several seconds, such as 15 seconds, to several minutes,        such as 15 minutes.    -   Dilution: does the system make the correct dilution, including        sample volume, reagent volume, and mixing. In various        embodiments, aspects of a flow cytometry system such as optical        density can be tested. In various embodiments, optical density        can be tested using a colored dye sample, such as red dye.    -   System Chemistry: do the reagents interact appropriately with        the sample.    -   Fluidics: does the diluted sample present to the detection        method appropriately. In various embodiments, aspects of a flow        cytometry system such as flow rate can be tested.    -   Sensors: do the cells interact with the detection system in the        proper manner. In various embodiments, aspects of a flow        cytometry system such as extinction channel (EXT), low angle        forward light scatter channel (FSL), right angle scatter channel        (RAS), high angle forward light scatter channel (FSH), and/or        time-of-flight channel (TOF), can be tested.    -   Signal Processing: do the cell signals present with appropriate        signal to noise.    -   Classification Algorithm: do the cells present appropriately to        the detection system so that the algorithm identifies the        populations correctly.    -   Results: does the system provide precise and accurate results.

The following will now describe the blank run operation 104 of FIG. 1 .The fluidic sub-system of a flow cytometer is responsible for combiningwhole blood samples with reagents, mixing them, and moving them to thelaser optics sub-system. The fluidic sub-system of a flow cytometeralways contains reagents and generally requires maintenance proceduresto ensure it is ready to run a sample. When a diagnostic system has onerun per day or one run every few days, the fluidic sub-system is at riskof becoming “dirty” from, for example, protein, bacteria, stain, or saltconcentrations in the fluid lines. In accordance with aspects of thepresent disclosure, periodic flushes can be performed to keep thefluidic sub-system clean. In various embodiments, the periodic flushescan be performed by using “blank” runs, which are diagnostic system runsthat are performed as though a sample is present, but without any sampleactually being present. The results of these blank runs can be recorded108 and charted 110 to determine cleanliness of the fluidic sub-systemand to evaluate any trends in the recorded data. In various embodiments,blank run operations 104 can be performed automatically by thediagnostic system on a regular basis or as scheduled or requested.Because blank runs are performed as though a sample is present, reagentsare used in blank runs and are consumed more quickly.

Blank runs 104 can measure cleanliness of the diagnostic system andensure there is no sample carryover from one run to the next. Inparticular, in a blank run, diagnostic system sensor values will shiftif there is buildup in the optical path or other wear conditions invarious components. Trending of the blank run data allows for an ongoingcleanliness checks using historical trends. Some cleanliness problemscan be corrected. For example, operator can run a bleach sample in thediagnostic system to remove buildup in the optical path, or can run abiocide sample to kill bacteria colonies that may have infiltrated thediagnostic system. Thus, the blank run 104 can identify such conditionsand alert an operator to actions to address such conditions. In variousembodiments, some diagnostic system measurements can use the blank runas a reference to self-calibrate results, such as in the transmittancemeasurement for hemoglobin where the blank value is used in a ratio withthe sample value to determine the optical transmittance in accordancewith Beer's Law.

The following will describe aspects of the patient data run 106 of FIG.1 . Non-fresh sample control approaches have limited capacity toevaluate reagent chemistry and algorithm effects. In view of suchlimitations, and in accordance with aspects of the present disclosure,quality control procedures can be augmented with feedback controlapproaches based on patient-data of multiple patients. An example ofusing patient data to determine normal and abnormal data ranges isdescribed in U.S. Patent Application Publication No. 2015/0025808, whichis hereby incorporated by reference herein in its entirety, and whichpersons skilled in the art will understand.

In various embodiments, the patient run operation 106 involves averagingsequential patient samples using various averaging techniques todetermine data ranges and trends based on patient samples. This data canbe stored in the database 108 and can be used to generate control chart110. In various embodiments, control chart rules 112 can be applied todetermine if the diagnostic system is in or out of control by comparinga patient sample result to the patient-data-based control chart. Invarious embodiments, patient run operations 106 for quality controlpurposes can be performed automatically by the diagnostic system on aregular basis or as scheduled or requested.

In various embodiments, a separate control chart 110 can be generatedfor each animal species supported by the diagnostic system, such as acanine control chart, or a feline control chart. In various embodiments,calibration adjustments can be performed based on the species-specificpopulation results.

Accordingly, described herein is an integration of on-board automatedbead analysis, automated blank runs, and/or trended patient samples (byspecies), which provides a new approach to determine not only that thediagnostic system is in control, but also which component is failing ifit is not in control. Actionable guidance can be automatically providedto operators if manual interaction is required. Or if the diagnosticsystem can be automatically adjusted to fall within intended parameters,the diagnostic system will perform the automatic adjustment and informthe operator accordingly.

The embodiments disclosed herein are examples of the disclosure and maybe embodied in various forms. For instance, although certain embodimentsherein are described as separate embodiments, each of the embodimentsherein may be combined with one or more of the other embodiments herein.Specific structural and functional details disclosed herein are not tobe interpreted as limiting, but as a basis for the claims and as arepresentative basis for teaching one skilled in the art to variouslyemploy the present disclosure in virtually any appropriately detailedstructure. Like reference numerals may refer to similar or identicalelements throughout the description of the figures.

The phrases “in an embodiment,” “in embodiments,” “in variousembodiments,” “in some embodiments,” “in various embodiments,” or “inother embodiments” may each refer to one or more of the same ordifferent embodiments in accordance with the present disclosure. Aphrase in the form “A or B” means “(A), (B), or (A and B).” A phrase inthe form “at least one of A, B, or C” means “(A); (B); (C); (A and B);(A and C); (B and C); or (A, B, and C).”

Any of the herein described methods, programs, algorithms or codes maybe converted to, or expressed in, a programming language or computerprogram. The terms “programming language” and “computer program,” asused herein, each include any language used to specify instructions to acomputer, and include (but is not limited to) the following languagesand their derivatives: Assembler, Basic, Batch files, BCPL, C, C+, C++,Delphi, Fortran, Java, JavaScript, machine code, Matlab, operatingsystem command languages, Pascal, Perl, PL1, Python, scriptinglanguages, Visual Basic, metalanguages which themselves specifyprograms, and all first, second, third, fourth, fifth, or furthergeneration computer languages. Also included are database and other dataschemas, and any other meta-languages. No distinction is made betweenlanguages which are interpreted, compiled, or use both compiled andinterpreted approaches. No distinction is made between compiled andsource versions of a program. Thus, reference to a program, where theprogramming language could exist in more than one state (such as source,compiled, object, or linked) is a reference to any and all such states.Reference to a program may encompass the actual instructions and/or theintent of those instructions.

It should be understood that the foregoing description is onlyillustrative of the present disclosure. Various alternatives andmodifications can be devised by those skilled in the art withoutdeparting from the disclosure. Accordingly, the present disclosure isintended to embrace all such alternatives, modifications and variances.The embodiments described with reference to the attached drawing figuresare presented only to demonstrate certain examples of the disclosure.Other elements, steps, methods, and techniques that are insubstantiallydifferent from those described above and/or in the appended claims arealso intended to be within the scope of the disclosure.

The systems described herein may also utilize one or more controllers toreceive various information and transform the received information togenerate an output. The controller may include any type of computingdevice, computational circuit, or any type of processor or processingcircuit capable of executing a series of instructions that are stored ina memory. The controller may include multiple processors and/ormulticore central processing units (CPUs) and may include any type ofprocessor, such as a microprocessor, digital signal processor,microcontroller, programmable logic device (PLD), field programmablegate array (FPGA), or the like. The controller may be located within adevice or system at an end-user location, may be located within a deviceor system at a manufacturer or servicer location, or may be a cloudcomputing processor located at a cloud computing provider. Thecontroller may also include a memory to store data and/or instructionsthat, when executed by the one or more processors, causes the one ormore processors to perform one or more methods and/or algorithms.

Any of the herein described methods, programs, algorithms or codes maybe converted to, or expressed in, a programming language or computerprogram. The terms “programming language” and “computer program,” asused herein, each include any language used to specify instructions to acomputer, and include (but is not limited to) the following languagesand their derivatives: Assembler, Basic, Batch files, BCPL, C, C+, C++,Delphi, Fortran, Java, JavaScript, machine code, operating systemcommand languages, Pascal, Perl, PL1, scripting languages, Visual Basic,metalanguages which themselves specify programs, and all first, second,third, fourth, fifth, or further generation computer languages. Alsoincluded are database and other data schemas, and any othermeta-languages. No distinction is made between languages which areinterpreted, compiled, or use both compiled and interpreted approaches.No distinction is made between compiled and source versions of aprogram. Thus, reference to a program, where the programming languagecould exist in more than one state (such as source, compiled, object, orlinked) is a reference to any and all such states. Reference to aprogram may encompass the actual instructions and/or the intent of thoseinstructions.

It should be understood that the foregoing description is onlyillustrative of the present disclosure. Various alternatives andmodifications can be devised by those skilled in the art withoutdeparting from the disclosure. Accordingly, the present disclosure isintended to embrace all such alternatives, modifications and variances.The embodiments described with reference to the attached drawing figuresare presented only to demonstrate certain examples of the disclosure.Other elements, steps, methods, and techniques that are insubstantiallydifferent from those described above and/or in the appended claims arealso intended to be within the scope of the disclosure.

What is claimed is:
 1. A hematology analyzer for point-of-care medicaldiagnostics, comprising: one or more sub-systems associated withanalyzing (1) a blood sample of a subject and (2) a synthetic qualitycontrol material stored at a room temperature, the one or moresub-systems having one or more operating parameters; a database; one ormore processors; and at least one memory storing instructions which,when executed by the one or more processors, cause the hematologyanalyzer to: initiate a quality control procedure in the hematologyanalyzer utilizing the synthetic quality control material, the qualitycontrol procedure comprising: analyzing the synthetic quality controlmaterial; generating quality control results based at least in part onanalyzing the synthetic quality control material; storing the qualitycontrol results in the database; determining whether a parameter of theone or more operating parameters of the one or more sub-systems isout-of-tolerance based on the quality control results; and in responseto determining that the parameter of the one or more operatingparameters of the one or more sub-systems is out-of-tolerance, adjustingat least one sub-system of the one or more sub-systems to bring theout-of-tolerance parameter within tolerance; and subsequent toinitiating the quality control procedure, initiate a subject-dataoperation, the subject-data operation comprising analyzing the bloodsample of the subject.
 2. The hematology analyzer of claim 1, whereinthe database stores previous subject test results, the previous subjecttest results comprising results obtained from the hematology analyzeranalyzing samples from a plurality of subjects over time, wherein theinstructions, when executed by the one or more processors, further causethe hematology analyzer to: generate control results based on theprevious subject test results; determine whether a second parameter ofthe one or more operating parameters of the one or more sub-systems isout-of-tolerance based on the control results; in response todetermining that the second parameter of the one or more operatingparameters of the one or more sub-systems is out-of-tolerance, adjust atleast one sub-system of the one or more sub-systems to bring the secondparameter within tolerance.
 3. The hematology analyzer of claim 1,wherein the synthetic quality control material is positioned within anon-board storage container received by the hematology analyzer.
 4. Thehematology analyzer of claim 1, wherein the one or more sub-systemscomprises an optics system.
 5. The hematology analyzer of claim 1,wherein the one or more sub-systems comprises an electronics system. 6.The hematology analyzer of claim 1, wherein the hematology analyzer is aflow cytometry system, and wherein the one or more sub-systems compriseat least one of a fluidics system, an optics system, and an electronicssystem.
 7. The hematology analyzer of claim 6, wherein the one or moreoperating parameters include optical density, flow rate, extinctionchannel (EXT), low angle forward light scatter channel (FSL), rightangle scatter channel (RAS), high angle forward light scatter channel(FSH), and time-of-flight channel (TOF).
 8. The hematology analyzer ofclaim 1, wherein the instructions, when executed by the one or moreprocessors, further cause the hematology analyzer to store a pluralityof quality control results in the database, the plurality of qualitycontrol results associated with the quality control procedure performedover time.
 9. A system for point-of-care medical diagnostics, the systemcomprising: a flow cytometry hematology analyzer configured to analyzesubject samples and to analyze a consumable synthetic quality controlmaterial, the flow cytometry analyzer comprising one or more subsystems,wherein the flow cytometry hematology analyzer is structurallyconfigured to be in communication with a storage container storing aconsumable synthetic quality control material, the synthetic qualitycontrol material structurally configured to be stored at roomtemperature; a database configured to store quality control results overtime, the quality control results over time including results of theflow cytometry hematology analyzer analyzing the consumable syntheticquality control material over time; one or more processors; and at leastone memory storing instructions, which, when executed by the one or moreprocessors, cause the system to: (a) perform quality control proceduresby accessing the consumable synthetic quality control material from thestorage container; (b) analyze the consumable synthetic quality controlmaterial using the flow cytometry hematology analyzer; (c) generatequality control results; (d) store the generated quality control resultsin the database; (e) repeat steps (a)-(d) over time; (f) generatecontrol parameters based on the generated quality control results storedin the database; (g) determine whether a parameter of the controlparameters is out-of-tolerance based on the quality control results; and(h) in response to determining that the parameter of the controlparameters is out-of-tolerance, adjust at least one sub-system of theone or more sub-systems without user intervention to bring theout-of-tolerance parameter within tolerance.
 10. The system of claim 9,wherein the database stores previous subject test results, the previoussubject test results comprising results obtained from the flow cytometryhematology analyzer analyzing samples from a plurality of subjects overtime, wherein the instructions, when executed by the one or moreprocessors, further cause the system to: generate control results basedon the previous subject test results; determine whether a secondparameter of the control parameters is out-of-tolerance based on thecontrol results; and in response to determining that the secondparameter of the control parameters is out-of-tolerance, adjust at leastone sub-system of the one or more sub-systems to bring the secondparameter within tolerance.
 11. The system of claim 9, wherein the oneor more sub-systems comprises an optics system.
 12. The system of claim9, wherein the one or more sub-systems comprises an electronics system.13. The system of claim 9, wherein the control parameters includeoptical density, flow rate, extinction channel (EXT), low angle forwardlight scatter channel (FSL), right angle scatter channel (RAS), highangle forward light scatter channel (FSH), and time-of-flight channel(TOF).
 14. The system of claim 9, wherein the instructions, whenexecuted by the one or more processors, further cause the system toanalyze a blank sample using the flow cytometry hematology analyzer,wherein the flow cytometry hematology analyzer operates on the blanksample in a same manner that the flow cytometry hematology analyzeroperates on a subject sample.
 15. The system of claim 14, wherein theinstructions, when executed by the one or more processors, further causethe system to, based at least in part on results from analyzing theblank sample, provide a visual indication informing a user that the flowcytometry hematology analyzer should be cleaned.
 16. A method foroperating a hematology analyzer, the method comprising: receiving acontainer storing a synthetic quality control material to a hematologyanalyzer, the synthetic quality control material comprising one or morebeads structurally configured to be stored at room temperature;initiating a quality control procedure in the hematology analyzerutilizing the synthetic quality control material, the quality controlprocedure comprising: analyzing the synthetic quality control material;generating quality control results based at least in part on analyzingthe synthetic quality control material; storing the quality controlresults in a database; determining whether a parameter of one or moresub-systems of the hematology analyzer is out-of-tolerance based on thequality control results; and in response to determining that theparameter of the one or more sub-systems is out-of-tolerance, adjustingat least one sub-system of the one or more sub-systems without userintervention to bring the out-of-tolerance parameter within tolerance;and subsequent to initiating the quality control procedure, initiating asubject-data operation, the subject data operation comprising analyzinga blood sample of the subject.
 17. The method of claim 16, wherein theone or more sub-systems comprises an optics system.
 18. The method ofclaim 16, wherein the one or more sub-systems comprises an electronicssystem.
 19. The method of claim 16, wherein the hematology analyzer is aflow cytometry system, and wherein analyzing the synthetic qualitycontrol material comprises passing the synthetic quality controlmaterial through a cuvette.
 20. The method of claim 16, wherein theparameter is one of an optical density, flow rate, extinction channel(EXT), low angle forward light scatter channel (FSL), right anglescatter channel (RAS), high angle forward light scatter channel (FSH),and time-of-flight channel (TOF).
 21. The method of claim 20, furthercomprising analyzing a blank sample using the hematology analyzer,wherein the hematology analyzer operates on the blank sample in a samemanner that the hematology analyzer operates on a subject sample.